def test_object_noise():
np.random.seed(0)
object_noise = ObjectNoise()
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
points = LiDARPoints(points, points_dim=4)
input_dict = dict(points=points, gt_bboxes_3d=gt_bboxes_3d)
input_dict = object_noise(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d'].tensor
expected_gt_bboxes_3d = torch.tensor(
[[9.1724, -1.7559, -1.3550, 0.4800, 1.2000, 1.8900, 0.0505]])
repr_str = repr(object_noise)
expected_repr_str = 'ObjectNoise(num_try=100, ' \
'translation_std=[0.25, 0.25, 0.25], ' \
'global_rot_range=[0.0, 0.0], ' \
'rot_range=[-0.15707963267, 0.15707963267])'
assert repr_str == expected_repr_str
assert points.tensor.numpy().shape == (800, 4)
assert torch.allclose(gt_bboxes_3d, expected_gt_bboxes_3d, 1e-3)
def test_object_sample():
db_sampler = mmcv.ConfigDict({
'data_root': './tests/data/kitti/',
'info_path': './tests/data/kitti/kitti_dbinfos_train.pkl',
'rate': 1.0,
'prepare': {
'filter_by_difficulty': [-1],
'filter_by_min_points': {
'Pedestrian': 10
}
},
'classes': ['Pedestrian', 'Cyclist', 'Car'],
'sample_groups': {
'Pedestrian': 6
}
})
object_sample = ObjectSample(db_sampler)
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
CLASSES = ('car', 'pedestrian', 'cyclist')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
input_dict = dict(
points=points, gt_bboxes_3d=gt_bboxes_3d, gt_labels_3d=gt_labels)
input_dict = object_sample(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
expected_gt_bboxes_3d = torch.tensor(
[[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100],
[8.7314, -1.8559, -1.5997, 0.4800, 1.2000, 1.8900, 0.0100]])
expected_gt_labels_3d = np.array([-1, 0])
repr_str = repr(object_sample)
expected_repr_str = 'ObjectSample'
assert repr_str == expected_repr_str
assert points.shape == (1177, 4)
assert torch.allclose(gt_bboxes_3d.tensor, expected_gt_bboxes_3d)
assert np.all(gt_labels_3d == expected_gt_labels_3d)
def test_background_points_filter():
np.random.seed(0)
background_points_filter = BackgroundPointsFilter((0.5, 2.0, 0.5))
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
orig_points = points.copy()
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
extra_points = gt_bboxes_3d.corners.reshape(8, 3)[[1, 2, 5, 6], :]
extra_points[:, 2] += 0.1
extra_points = torch.cat([extra_points, extra_points.new_zeros(4, 1)], 1)
points = np.concatenate([points, extra_points.numpy()], 0)
points = LiDARPoints(points, points_dim=4)
input_dict = dict(points=points, gt_bboxes_3d=gt_bboxes_3d)
origin_gt_bboxes_3d = gt_bboxes_3d.clone()
input_dict = background_points_filter(input_dict)
points = input_dict['points'].tensor.numpy()
repr_str = repr(background_points_filter)
expected_repr_str = 'BackgroundPointsFilter(bbox_enlarge_range=' \
'[[0.5, 2.0, 0.5]])'
assert repr_str == expected_repr_str
assert points.shape == (800, 4)
assert np.equal(orig_points, points).all()
assert np.equal(input_dict['gt_bboxes_3d'].tensor.numpy(),
origin_gt_bboxes_3d.tensor.numpy()).all()
# test single float config
BackgroundPointsFilter(0.5)
# The length of bbox_enlarge_range should be 3
with pytest.raises(AssertionError):
BackgroundPointsFilter((0.5, 2.0))
def test_object_name_filter():
class_names = ['Pedestrian']
object_name_filter = ObjectNameFilter(class_names)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
CLASSES = ('Pedestrian', 'Cyclist', 'Car')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
gt_labels = np.array(gt_labels, dtype=np.long)
input_dict = dict(gt_bboxes_3d=gt_bboxes_3d.clone(),
gt_labels_3d=gt_labels.copy())
results = object_name_filter(input_dict)
bboxes_3d = results['gt_bboxes_3d']
labels_3d = results['gt_labels_3d']
keep_mask = np.array([name in class_names for name in gt_names])
assert torch.allclose(gt_bboxes_3d.tensor[keep_mask], bboxes_3d.tensor)
assert np.all(gt_labels[keep_mask] == labels_3d)
repr_str = repr(object_name_filter)
expected_repr_str = f'ObjectNameFilter(classes={class_names})'
assert repr_str == expected_repr_str
def test_object_sample():
db_sampler = mmcv.ConfigDict({
'data_root': './tests/data/kitti/',
'info_path': './tests/data/kitti/kitti_dbinfos_train.pkl',
'rate': 1.0,
'prepare': {
'filter_by_difficulty': [-1],
'filter_by_min_points': {
'Pedestrian': 10
}
},
'classes': ['Pedestrian', 'Cyclist', 'Car'],
'sample_groups': {
'Pedestrian': 6
}
})
np.random.seed(0)
object_sample = ObjectSample(db_sampler)
points = np.fromfile(
'./tests/data/kitti/training/velodyne_reduced/000000.bin',
np.float32).reshape(-1, 4)
annos = mmcv.load('./tests/data/kitti/kitti_infos_train.pkl')
info = annos[0]
rect = info['calib']['R0_rect'].astype(np.float32)
Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
annos = info['annos']
loc = annos['location']
dims = annos['dimensions']
rots = annos['rotation_y']
gt_names = annos['name']
gt_bboxes_3d = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
gt_bboxes_3d = CameraInstance3DBoxes(gt_bboxes_3d).convert_to(
Box3DMode.LIDAR, np.linalg.inv(rect @ Trv2c))
CLASSES = ('Pedestrian', 'Cyclist', 'Car')
gt_labels = []
for cat in gt_names:
if cat in CLASSES:
gt_labels.append(CLASSES.index(cat))
else:
gt_labels.append(-1)
gt_labels = np.array(gt_labels, dtype=np.long)
points = LiDARPoints(points, points_dim=4)
input_dict = dict(points=points,
gt_bboxes_3d=gt_bboxes_3d,
gt_labels_3d=gt_labels)
input_dict = object_sample(input_dict)
points = input_dict['points']
gt_bboxes_3d = input_dict['gt_bboxes_3d']
gt_labels_3d = input_dict['gt_labels_3d']
repr_str = repr(object_sample)
expected_repr_str = 'ObjectSample sample_2d=False, ' \
'data_root=./tests/data/kitti/, ' \
'info_path=./tests/data/kitti/kitti' \
'_dbinfos_train.pkl, rate=1.0, ' \
'prepare={\'filter_by_difficulty\': [-1], ' \
'\'filter_by_min_points\': {\'Pedestrian\': 10}}, ' \
'classes=[\'Pedestrian\', \'Cyclist\', \'Car\'], ' \
'sample_groups={\'Pedestrian\': 6}'
assert repr_str == expected_repr_str
assert points.tensor.numpy().shape == (800, 4)
assert gt_bboxes_3d.tensor.shape == (1, 7)
assert np.all(gt_labels_3d == [0])
def show_proj_det_result_meshlab(data,
result,
out_dir,
score_thr=0.0,
show=False,
snapshot=False):
"""Show result of projecting 3D bbox to 2D image by meshlab."""
assert 'img' in data.keys(), 'image data is not provided for visualization'
img_filename = data['img_metas'][0][0]['filename']
file_name = osp.split(img_filename)[-1].split('.')[0]
# read from file because img in data_dict has undergone pipeline transform
img = mmcv.imread(img_filename)
if 'pts_bbox' in result[0].keys():
result[0] = result[0]['pts_bbox']
elif 'img_bbox' in result[0].keys():
result[0] = result[0]['img_bbox']
pred_bboxes = result[0]['boxes_3d'].tensor.numpy()
pred_scores = result[0]['scores_3d'].numpy()
# filter out low score bboxes for visualization
if score_thr > 0:
inds = pred_scores > score_thr
pred_bboxes = pred_bboxes[inds]
box_mode = data['img_metas'][0][0]['box_mode_3d']
if box_mode == Box3DMode.LIDAR:
if 'lidar2img' not in data['img_metas'][0][0]:
raise NotImplementedError(
'LiDAR to image transformation matrix is not provided')
show_bboxes = LiDARInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
show_multi_modality_result(img,
None,
show_bboxes,
data['img_metas'][0][0]['lidar2img'],
out_dir,
file_name,
box_mode='lidar',
show=show)
elif box_mode == Box3DMode.DEPTH:
show_bboxes = DepthInstance3DBoxes(pred_bboxes, origin=(0.5, 0.5, 0))
show_multi_modality_result(img,
None,
show_bboxes,
None,
out_dir,
file_name,
box_mode='depth',
img_metas=data['img_metas'][0][0],
show=show)
elif box_mode == Box3DMode.CAM:
if 'cam2img' not in data['img_metas'][0][0]:
raise NotImplementedError(
'camera intrinsic matrix is not provided')
show_bboxes = CameraInstance3DBoxes(pred_bboxes,
box_dim=pred_bboxes.shape[-1],
origin=(0.5, 1.0, 0.5))
show_multi_modality_result(img,
None,
show_bboxes,
data['img_metas'][0][0]['cam2img'],
out_dir,
file_name,
box_mode='camera',
show=show)
else:
raise NotImplementedError(
f'visualization of {box_mode} bbox is not supported')
return file_name